One of the core problems of modern statistics and machine learning is to
approximate difficult-to-compute probability distributions. This problem is
especially important in probabilistic modeling, which frames all inference
about unknown quantities as a calculation about a conditional distribution.
In this tutorial we review and discuss variational inference (VI), a method
a that approximates probability distributions through optimization. VI has
been used in myriad applications in machine learning and tends to be faster
than more traditional methods, such as Markov chain Monte Carlo
sampling. Brought into machine learning in the 1990s, recent advances and easier
implementation have renewed interest and application of this class of
methods. This tutorial aims to provide both an introduction to VI with a
modern view of the field, and an overview of the role that probabilistic
inference plays in many of the central areas of machine learning.

The tutorial has three parts. First, we provide a broad review of
variational inference from several perspectives. This part serves as an
introduction (or review) of its central concepts. Second, we develop and
connect some of the pivotal tools for VI that have been developed in the
last few years, tools like Monte Carlo gradient estimation, black box
variational inference, stochastic approximation, and variational
auto-encoders. These methods have lead to a resurgence of research and
applications of VI. Finally, we discuss some of the unsolved problems in VI
and point to promising research directions.

Learning objectives;

Gain a well-grounded understanding of modern advances in variational
inference.

Understand how to implement basic versions for a wide class of models.

Understand connections and different names used in other related
research areas.

Understand important problems in variational inference research.

Target audience;

Machine learning researchers across all level of experience from first
year grad students to other more experienced researchers

Targeted at those who want to understand recent advances in
variational inference

Basic understanding of probability is sufficient

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